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Learning a fast and discriminative patch descriptor is a challenging topic in computer vision. Recently, many existing works focus on training various descriptor learning networks by minimizing a triplet loss (or its variants), which is expected to decrease the distance between each positive pair and increase the distance between each negative pair. However, such an expectation has to be lowered due to the non-perfect convergence of network optimizer to a local solution. Addressing this problem and the open computational speed problem, we propose a Descriptor Distillation framework for local descriptor learning, called DesDis, where a student model gains knowledge from a pre-trained teacher model, and it is further enhanced via a designed teacher-student regularizer. This teacher-student regularizer is to constrain the difference between the positive (also negative) pair similarity from the teacher model and that from the student model, and we theoretically prove that a more effective student model could be trained by minimizing a weighted combination of the triplet loss and this regularizer, than its teacher which is trained by minimizing the triplet loss singly. Under the proposed DesDis, many existing descriptor networks could be embedded as the teacher model, and accordingly, both equal-weight and light-weight student models could be derived, which outperform their teacher in either accuracy or speed. Experimental results on 3 public datasets demonstrate that the equal-weight student models, derived from the proposed DesDis framework by utilizing three typical descriptor learning networks as teacher models, could achieve significantly better performances than their teachers and several other comparative methods. In addition, the derived light-weight models could achieve 8 times or even faster speeds than the comparative methods under similar patch verification performances. [ABSTRACT FROM AUTHOR]

Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled images and generalize over the new tasks. In this paper, motivated by the idea of Fisher Score, we propose a Discriminative Local Descriptors Attention model that uses the ratio of intra-class and inter-class similarity to adaptively highlight the representative local descriptors without introducing any additional parameters, while most of the existing local descriptors based methods utilize the neural networks that inevitably involve the tedious parameter tuning. Experiments on four benchmark datasets show that our method achieves higher accuracy compared with the state-of-art approaches for few-shot learning. Specifically, our method is optimal on the CUB-200 dataset, and outperforms the second best competitive algorithm by 4.12 % and 0.49 % under the 5-way 1-shot and 5-way 5-shot settings, respectively. [ABSTRACT FROM AUTHOR]

We construct a novel descriptor called anisotropic wavelet energy decomposition descriptor (AWEDD) for non-rigid shape analysis, based on anisotropic diffusion geometry. We first extend the Dirichlet energy of the vertex coordinate function to an anisotropic version, then use multiscale anisotropic spectral manifold wavelets to decompose the Dirichlet energy to all vertices and collect local energy at each vertex to form AWEDD. AWEDD simultaneously encodes multiscale extrinsic and intrinsic shape features, which are more informative and robust than purely intrinsic or extrinsic descriptors. And the introduction of anisotropy endows AWEDD with stronger abilities of feature discrimination and intrinsic symmetry identification. Our results demonstrate that AWEDD is more discriminative than current state-of-the-art descriptors. In addition, we show that AWEDD is an excellent choice of the initial inputs for various shape analysis approaches, such as functional map pipelines and deep convolutional architectures. [ABSTRACT FROM AUTHOR]

Multi-scale feature fusion has been widely used in handcrafted descriptors, but has not been fully explored in deep learning-based descriptor extraction. Simple concatenation of descriptors of different scales has not been successful in significantly improving performance for computer vision tasks. In this paper, we propose a novel convolutional neural network, based on center-surround adaptive multi-scale feature fusion. Our approach enables the network to focus on different center-surround scales, resulting in improved performance. We also introduce a novel regularization technique that uses second-order similarity to constrain the learning of local descriptors, based on the symmetric property of the similarity matrix. The proposed method outperforms single-scale or simple-concatenation descriptors on two datasets and achieves state-of-the-art results on the Brown dataset. Furthermore, our method demonstrates excellent generalization ability on the HPatches dataset. Our code is released on GitHub: https://github.com/Leung-GD/AFSRNet/tree/main. [ABSTRACT FROM AUTHOR]

Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn’t expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes. [ABSTRACT FROM AUTHOR]

Deep learning has been successfully exploited to various computer vision tasks, which depend on abundant annotations. The core goal of few-shot learning, in contrast, is to learn a classifier to recognize new classes from only a few labeled examples that produce a key challenge of visual recognition. However, most of the existing methods often adopt image-level features or local monodirectional manner-based similarity measures, which suffer from the interference of non-dominant objects. To tackle this limitation, we propose a Bi-Directional Local Alignment (BDLA) approach for the few-shot visual classification problem. Specifically, building upon the episodic learning mechanism, we first adopt a shared embedding network to encode the 3D tensor features with semantic information, which can effectively describe the spatial geometric representation of the image. Afterwards, we construct a forward and a backward distance by exploring the nearest neighbor search to determine the semantic region-wise feature corresponding to each local descriptor of query sets and support sets. The bi-directional distance can encourage the alignment between similar semantic information while filtering out the interference information. Finally, we design a convex combination to merge the bi-directional distance and optimize the network in an end-to-end manner. Extensive experiments also show that our proposed approach outperforms several previous methods on four standard few-shot classification datasets. [ABSTRACT FROM AUTHOR]

Stock price trend prediction is to seek profit maximum of stock investment by estimating future stock price tendency. Nevertheless, it is still a tough task due to noisy and non-stationary properties of stock market. Thus, it is important how to relieve such negative effects and to improve prediction accuracy. In this paper, we leverage four diverse local descriptors in short durations to alleviate noisy fluctuations of stock price. In detail, piecewise aggregate approximation (PAA) collects relatively stable average values; the derivatives of short-time series reflect the change ratio of stock price; the slope implies the short-time price trend; hog-1D aggregates different oriented gradients into histograms in a statistical fashion. They provide diverse and comprehensive cues about the stock price series across different aspects. Building upon such local descriptors, we propose a multi-scale local cues and hierarchical attention-based LSTM model (MLCA-LSTM) to capture the underlying price trend patterns. It has two advantages: 1) multi-scale information is further enriched by performing different scale sliding windows over stock price series to induce diverse local descriptors, 2) temporal dependency and multi-scale interactions are jointly attended and aggregated through the hierarchical attention mechanism and multi-branch LSTM structure. Experiments on the real stock price data confirm the efficacy of the proposed model as compared to the state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

Local descriptor is an important upstream component in computer vision tasks. Despite considerable advances with deep learning-based descriptors, recent descriptors are not robust enough to handle widespread viewpoint changes in image matching tasks such as localization and 3D reconstruction. In this study, SDNet, a robust descriptor utilizing spatial adversarial perturbations, trained with a novel dynamic probabilistic weighting loss to enhance performance under such challenges. First, to increase the robustness and generalization ability of the network across spatially transformed instances, a innovative module for generating hard negative samples via spatial adversarial perturbations is designed. By maximizing adversarial loss, this module generates more complex patches, significantly enhancing the geometric robustness of the descriptor. Importantly, this module integrates seamlessly with existing patch-based descriptors without necessitating extra training data. Second, to mitigate the imbalance in the matching relationship between generated positive and negative pairs, the label weighted triplet loss is proposed, which markedly improves descriptor performance. Third, a comprehensive theoretical analysis of preceding studies is carried out from a gradient perspective, and a probabilistic dynamic weighting approach that adaptively emphasizes weighting functions with higher likelihoods is proposed to improve training performance of the descriptor. Extensive experiments are carried out on mainstream datasets. These comprehensive experiments demonstrate the effectiveness of SDNet, and the proposed method achieves significant improvements on the UBC, HPatches and ETH datasets, outperforming current state-of-the-art methods. The code is available at https://github.com/webd111/sdnet. [ABSTRACT FROM AUTHOR]

Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal infrared images and RGB images, the existing research on self-supervised monocular depth estimation has performance limitations. Therefore, in this study, we propose a novel Self-Guided Framework using a Pseudolabel predicted from RGB images. Our proposed framework, which solves the problem of appearance matching loss in the existing framework, transfers the high accuracy of Pseudolabel to the thermal depth estimation network by comparing low- and high-level pixels. Furthermore, we propose Patch-NetVLAD Loss, which strengthens local detail and global context information in the depth map from thermal infrared imaging by comparing locally global patch-level descriptors. Finally, we introduce an Image Matching Loss to estimate a more accurate depth map in a thermal depth network by enhancing the performance of the Pseudolabel. We demonstrate that the proposed framework shows significant performance improvement even when applied to various depth networks in the KAIST Multispectral Dataset. [ABSTRACT FROM AUTHOR]

Local feature patterns are conspicuous and are widely used in computer vision, especially in face recognition and retrieval. However, a statistical descriptor that can be used in various scenarios and effectively present the detailed local discrimination information of face images is a challenging and exploring task even if deep learning technology is widelyspread. In this study, we propose a novel local pattern descriptor called the Local Clustering Pattern (LCP) in high-order derivative space for facial recognition and retrieval. Unlike prior methods, LCP exploits the concept of clustering to analyze the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels to encode the local descriptor for facial recognition. There are three tasks (1) Local Clustering Pattern (LCP), (2) Clustering Coding Scheme, (3) High-order Local Clustering Pattern. To generate local clustering pattern, the local derivative variations with multi-direction are considered and that are integrated on rectangular coordinate system with the pairwise combinatorial direction. Moreover, to generate the discriminative local pattern, the features of local derivative variations are transformed from the rectangular coordinate system into the polar coordinate system to generate the characteristics of magnitude (m) and orientation (θ ). Then, we shift and project the features (m and θ ), which are scattered in the four quadrants of polar coordinate system, into the first quadrant of polar coordinates to strengthen the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels. To encode the local pattern, we consider the spatial relationship between reference and its adjacent pixels and fuse the clustering algorithm into the coding scheme by utilizing the relationship of intra- and inter-classes in a local patch. In addition, we extend the LCP from low- into high-order derivative space to extract the detailed and abundant information for facial description. LCP efficiently encodes the feature of a local region that is discriminative the inter-classes and robust the intra-class of the related pixels to describe a face image. This study has three main contributions: (1) we generate the novel features with magnitude (m) and orientation (θ ) based on the pairs of the derivative variations to describe the characteristics of each pixel, (2) we shift and project the features from four quadrants of polar coordinate system into the first quadrant of polar coordinates to strengthen the relationship of intra- and inter-classes between pixels in a local patch, (3) we exploit the concept of clustering, which considers the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels, to encode the local descriptor in a polar coordinate system for facial recognition and retrieval. Experimental results show that LCP outperforms the existing descriptors (LBP, ELBP LDP, LTrP, LVP, LDZP, LGHP) on six public datasets (ORL, Extend Yale B, CAS PEAL, and LFW, CMU-PIE and FERET) for both face recognition and retrieval tasks. Moreover, we further compare the proposed facial descriptor with the popular deep convolutional neural networks to demonstrate the discrimination of the extracted features and applicability of our approach. [ABSTRACT FROM AUTHOR]

Many of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors. In 2D images, the use of binary representations for visual tasks has shown that, with lower computational requirements, they can obtain a performance comparable to classic real-value techniques. However, these achievements have not been fully translated to 3D images, where research is mainly focused on real-value approaches. Thus, in this paper, we propose a keypoint detector and local descriptor based on 3D binary patterns. The experimentation demonstrates that our proposal is competitive against state-of-the-art techniques, while its processing can be performed more efficiently. [ABSTRACT FROM AUTHOR]

• Strengthen the descriptor by effectively combing features at different levels of CNN. • Capture the spatial information of a local patch with spatial context pyramid. • Comparable performance with state-of-the-art and comprehensive ablation experiments. Despite that efforts have shifted to learning local descriptors with convolutional neural network (CNN) from hand-crafted realm, the inherent feature hierarchy within CNN has been rarely explored. To increase both the invariant and discriminative abilities of the CNN-based local descriptors by making use of the complementary representation powers of the feature maps at different levels of CNN, in this paper, we design a multi-level feature aggregation (MLFA) module to communicate information across pyramid levels effectively. Then, each level extracts a feature vector after feature fusion and the final descriptor concatenates these outputs. Moreover, to leverage the spatial structure within a local patch, we propose a novel spatial context pyramid (SCP) module to capture the spatial information. SCP is devised in a residual manner and only several additional parameters are introduced to the model. We implement our algorithm based on the HardNet framework and carry out comprehensive evaluation on the UBC Phototour, HPatches and ETH datasets. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art ones. Ablation study is also provided to show the effectiveness of each component. [ABSTRACT FROM AUTHOR]

The complementary fusion of global and local features can effectively improve the performance of image retrieval. This article proposes a new local texture descriptor, combined with statistical modeling in transform domain for texture image retrieval. The proposed local descriptor calculates the eight directions of the central pixel by using the relationship between the central pixel and the neighboring pixels in six directions, which is called the local eight direction pattern (LEDP). In the texture image retrieval system of this article, the feature extraction part combines global statistical features and local pattern features. Among them, both the relative magnitude (RM) sub-band coefficients and relative phase (RP) sub-band coefficients are modeled as wrapped Cauchy (WC) distribution in the dual-tree complex wavelet transform (DTCWT) domain, and the global statistical features employ the parameters of this model; while the local pattern features respectively choose the local binary pattern (LBP) histogram features in the spatial domain and the LEDP histogram features of each direction sub-band in the DTCWT domain. On the other hand, the similarity measurement selects matching distances for different features and combines them in the form of convex linear optimization. Texture image retrieval experiments are conducted in the Corel-1k database (DB1), Brodatz texture database (DB2) and MIT VisTex texture database (DB3), respectively. Experimental results show that, compared with the best existing methods, the approach proposed in this article has achieved better retrieval performance.

In this paper, a novel face recognition approach based on strings of successive values (SSV) is presented. In contrast to most of the existing local descriptors which encode only a limited number of pixels included in a mask, the strings extract more discriminative information over the whole face region, by moving from the current pixel to the next one, and to the other next, and so on, according to the variations of their intensities. Therefore, the SSV can be stopped in any place of the face area, which allows us to encode more edge information and texture information than the existing methods. The proposed face recognition scheme requires several steps. Firstly, the images are divided into non-overlapping sub-regions from which the strings are extracted since each pixel produces two different strings. Thereafter, the dictionary of visual words is created to reduce the number of strings obtained from each patch of the image. Therefore, the face image is described only by visual words, because each string is replaced by its nearest dictionary word. As a result, the occurrence of visual words is computed in a histogram as a face descriptor. Finally, the recognition is performed by using the nearest neighbor classifier with the Hellinger distance. The effectiveness of the proposed approach is evaluated on three different databases, and the experimental results show that the recognition performances achieved are competitive or even outperform the literature state of the art methods. [ABSTRACT FROM AUTHOR]

In this work, we propose a new set of H ∞ deconvolution filtering of 2-D color image using feature extraction of local descriptor and Fornasini-Machesini II (FM-II) model. The principal goal is to design 2-D deconvolution filter to reconstruct the noisy color image with the minimal information extracted from local Krawtchouk moment, Moreover, the filtering error system is asymptotically stable and satisfy the H ∞ performance index. the sufficient condition is given to ensure the H ∞ performance of the filtering error system through the Lyapunov theory, and the local Krawtckouk moment to give the feature extraction according to the order defined in advance instead of the global color image. Moreover, the 2-D deconvolution filter is designed to achieve the H ∞ performance index which the filter parameters are determined with certain optimization resolution. Finally, simulation example is provided to demonstrate the usefulness of the proposed design methods. [ABSTRACT FROM AUTHOR]

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor. [ABSTRACT FROM AUTHOR]

The article presents a parallel version of the detector of extremal key points, which are used to describe, analyze and compare digital images by local descriptors. Local descriptors are determined in neighborhoods of the extremal key points. The orientation of the descriptors are found with aid of Histograms of Oriented Gradient. The specificity of the parallel architecture of NVIDIA graphics cards has been taken into account in the developed version, oriented to the implementation on CUDA. It accelerated the calculation of the extremal key points by several orders. Computation of the not oriented extremal key points for images of the FullHD-size on the budget graphics card takes 5–6 ms. The oriented extremal key points are computed within 11–12 ms.

Protein–protein interactions (PPIs) help to elucidate the molecular mechanisms of life activities and have a certain role in promoting disease treatment and new drug development. With the advent of the proteomics era, some PPIs prediction methods have emerged. However, the performances of these PPIs prediction methods still need to be optimized and improved. In order to optimize the performance of the PPIs prediction methods, we used the dropout method to reduce over-fitting by deep neural networks (DNNs), and combined with three types of feature extraction methods, conjoint triad (CT), auto covariance (AC) and local descriptor (LD), to build DNN models based on amino acid sequences. The results showed that the accuracy of the CT, AC and LD increased from 97.11% to 98.12%, 96.84% to 98.17%, and 95.30% to 95.60%, respectively. The loss values of the CT, AC and LD decreased from 27.47% to 14.96%, 65.91% to 17.82% and 36.23% to 15.34%, respectively. Experimental results show that dropout can optimize the performances of the DNN models. The results can provide a resource for scholars in future studies involving the prediction of PPIs. The experimental code is available at https://github.com/smalltalkman/hppi-tensorflow. [ABSTRACT FROM AUTHOR]

This paper proposes a novel high-performance dynamic inverse distance weighting based local descriptor (DIDWLD) for facial recognition. Studies proposed thus far have focused on finding local descriptors that can represent the texture of the face best. However, the robustness of the descriptors against rotational variances and noise affects have been largely omitted. Thus, this study does not only concern with proposing a high-discriminative descriptor, but also a robust one against rotational changes and noise affects. DIDWLD mainly basis on Inverse Distance Weighting (IDW). That is, for each pixel in the image, a new descriptive value is calculated, taking into account the intensity values of the neighboring pixels and their distance to the reference pixels. A dynamic distance-decay parameter is applied throughout the image rather than keeping it uniform as done in ordinary IDW. The calculated descriptor is independent of the changes in the rotation. Because, when calculating the descriptor, the intensity values of the surrounding pixels with their distances to the reference pixel are taken into consideration, yet their directional relation to the reference pixel is ignored. Furthermore, when a pixel is suffered to noise, inherently, its neighboring pixels are also affected. Hence, by taking into account the effect of the surrounding pixels and also the original intensity value of the pixel, the degrading impact of noise on recognition performance is mitigated. The results of extensive simulations show the remarkable and competitive performance of the proposed method regarding recognition accuracy, and robustness against rotational variances and, noise effects. [ABSTRACT FROM AUTHOR]

In the last decades, iris features have been widely used in biometric systems. Because iris features are virtually unique for each person, their usage is highly reliable. However, biometric systems based on iris features are not completely fraud-resistant, as most systems use static images and do not distinguish between a live iris and a photograph. The iris structure and texture change with light variations, and traditional techniques for iris recognition always identify the iris texture in a controlled environment. However, in uncontrolled environments, live irises are recognized by their dynamic response to light: If the light changes, the pupils dilate or contract, and their texture dynamically changes. If a biometric system can identify people during the constriction or dilation time interval, that system will be more fraud-resistant. This paper proposes a new methodology to evaluate the "dynamic texture" from iris image sequences (motion analysis) and measure the discriminant power of these features for biometric system applications. We propose two new dynamic descriptors—dynamic local mapped pattern and dynamic sampled local mapped pattern—which are extensions of the local mapped pattern previously published for texture classification. We applied our proposed dynamic texture descriptors in a sequence of iris images segmented from video under light variation. Then, we compared our results with the well-known dynamic texture descriptor local binary pattern from three orthogonal planes (LBP-TOP). We used statistical measures to evaluate the performance of both descriptors and concluded that our methodology performed better than the LBP-TOP. Moreover, our descriptors can extract dynamic textures faster than the LBP-TOP. [ABSTRACT FROM AUTHOR]

This paper proposes a new local image descriptor for Finger Knuckle Print Recognition Systems (FKPRS), named Log-Gabor Binarized Statistical Image Features descriptor (LGBSIF). The idea of LGBSIF is based on the image Log-Gabor wavelet representation and the Binarized Statistical Image Features (BSIF). Initially, the Region of Interest (ROI) of the FKP images are analyzed with a 1D Log-Gabor wavelet to extract the preliminary features that are presented by both the real and imaginary parts of the filtered image. The main motive of the LGBSIF is to enhance the Log-Gabor real and imaginary features by applying the BSIF coding method. Secondly, histograms extracted from the encoded real and imaginary images respectively are concatenated in one large feature vector. Thirdly, the PCA+LDA technique is used to reduce the dimensionality of this feature and enhance its discriminatory power. Finally, the Nearest Neighbor Classifier that uses the Cosine distance is employed for the matching process. The evaluation of the performance of the proposed system is done on the Poly-U FKP database. However, the experimental results have shown that the proposed system achieves better results than other state-of-the-art systems and confirmed the tenacity of the proposed descriptor. Further, the results also prove that the performance efficiency of the introduced system in terms of recognition rate (Rank1) and equal error rate (EER) are 100% and 0% for both modes of identification and verification respectively. [ABSTRACT FROM AUTHOR]

Steel surface demonstrates various sorts of defects due to the production technique and environment. The appearance of defect is in much more random pattern than that of the normal texture image. Therefore, it is challenging to capture the discriminant information to categorize the defects. The defect image is out of image registration in grayscale, and thus, the local descriptor is inclined to be utilized for feature extraction. In the previous works, involving a local descriptor for categorizing the defect images, the thresholding operator participates in the hand-crafted feature extraction, such as local binary patterns and histogram of oriented gradient, leading to sub-optimal features. By introducing the learning mechanism into the construction of local descriptor, a novel algorithm named discriminant manifold regularized local descriptor (DMRLD) is proposed to conduct the defect classification task in this paper. First, the DMRLD computes the dense pixel difference vector (DPDV) to draw the local information of defect images. Then, the manifold of these DPDVs can be constructed by searching for a number of linear models to represent the feature. In order to enhance the discriminant ability of the feature, a projection on the manifold is learned for achieving a low-dimensional subspace. Finally, the manifold distance defined in the subspace can accomplish the matching task to get the category of the defect image. The proposed algorithm is first applied on the Kylberg texture dataset to evaluate the texture feature extraction performance, and then the experiments on the real steel surface defect dataset are conducted to illustrate the effectiveness of DMRLD compared with other local descriptors.

Finger vein is a new and secure biometric for personal authentication due to its line-structure network with abundant local and orientation features. However, these features cannot be well represented by the existing local descriptors. To effectively utilize these rich orientation features in finger vein images, this paper proposes a new local descriptor, namely, pyramid histogram of double competitive pattern (PHDCP). For a finger vein image, the PHDCP first obtains a bank of filtered images using Gabor filters with large kernel size and rich orientations, by which the local line features are captured. Then, the orientation orders with the largest and smallest responses, which are the most robust features, are selected to generate an encoded map. Finally, a column-partition-based pyramid histogram extraction method is presented to capture the hierarchical features from the encoded image. Numerical experiments are conducted on two public finger vein data sets, MMCBNU_6000 and UTFVP. The experimental results demonstrate that the proposed PHDCP performs much better than the existing local descriptors.

Robustness is an important factor for background modelling on various scenarios. Current pixel‐based adaptive segmentation method cannot effectively tackle diverse objects simultaneously. To address this problem, in this study, a background modelling method using discriminative motion representation is proposed. Instead of simple usage of intensity to construct the background model, the proposed method extracts a new local descriptor which uses a weighted combination of differential excitations for each pixel to enhance the discriminability of pixels. On the basis of this background model, different categories of objects can be quickly identified by a simple but effective classification rule and accurately be represented in background model by a smart selection of updating strategies. Therefore, the authors’ background modelling method can generate complete representation for static objects and decrease false detection caused by dynamic background or illumination variations. Extensive experiments have been conducted to demonstrate that the proposed method obtains more advantages of foreground detection than the state‐of‐the‐art methods. In addition, the proposed method provides a computational efficient algorithm for foreground detection tasks.